Analyzing performance of fibers and fiber connections using long-term historical data

ABSTRACT

Systems, methods, and computer-readable media are provided for logging long-term data and analyzing the long-term data with short-term data to determine the health of fiber connections in an optical network. A method, according to one implementation, includes a step of obtaining data associated with performance of fiber connections of an optical network. The fiber connections include at least an inter-node fiber connecting two adjacent network nodes and an intra-node fiber connection connecting two photonic devices within each of the two adjacent network nodes. The method further includes the step of logging the data over time as historical data and then analyzing the health of the fiber connections based on the historical data and newly-obtained data. Also, the method includes displaying a report on an interactive user interface, whereby the report is configured to show the health of the fiber connections.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to fiber optics. Moreparticularly, the present disclosure relates to systems and methods forthe analysis of performance of fibers and fiber connections of anoptical network from long-term historical data and immediate data.

BACKGROUND OF THE DISCLOSURE

Optical networks are implemented with fiber optics where optical fibersare used to connect sites (e.g., Central Offices (COs), data centers,huts, repeaters, etc.) and associated Network Elements (NEs). A networkelement can include, for example, a fiber patch cord that connects anoptical interface to a fiber distribution shelf to external fiber thatphysically connects the site to another site. The external fiber caninclude buried cable, aerial cables, and the like. Over the lifetime ofoperation of an optical network, various activities may occur, such asfiber cuts, fiber splices, modification of connections (fiber patchcords), environmental conditions, and other activities that can have anegative effect on the optical network and specifically the opticalfiber.

All optical systems have mechanisms to monitor real-time performancemetrics and raise alarms when there are fiber or fiber connectionproblems. Traditionally, the monitoring and alarming system for fiberand fiber connections is reactive, i.e., alarms are raised whenhard-coded pass/fail criteria are not met such as threshold crossingsfor back reflection or threshold crossing for minimum allowed power.Many systems also focus on present day metrics, without much analysisand/or visibility into historical values.

Recently, proactive approaches are being explored and implemented.Machine Learning (ML) algorithms for predicting failure of line fibersand fiber aging mechanism are gaining interest. However, the output ofthe ML predictions is typically “0” or “1” for future failures withoutdetails of the reasoning available to the network operators.Furthermore, ML algorithms act as black boxes and do not readily explainthe reason for prediction/classification. Without the proper reasoning,operators may struggle to deal with False Positives and waste efforts oninvestigating/replacing non-issue fibers. Other approaches also focus onexternal line fibers (i.e., external fiber between physical sites) andfocusing on power monitoring and periodic Optical Time DomainReflectometry (OTDR) readings. However, existing OTDR monitoringapproaches only comparing a current OTDR trace with a baseline trace.

Conventional optical systems do not typically provide historical dataand data analysis. However, historical data can be useful forunderstanding the potential risks of a fibers and fiber connections, andthe overlaid services. Even though Performance Monitoring (PM)/alarmlogging has started to be implemented, the logged data has not beenutilized in conventional systems for analyzing fiber health. Also, thisdata is not made available for display in a user interface. However, asdescribed in the present disclosure, historical data can be used inorder to monitor trend of fiber connection performance from baselinemetrics established when a fiber was first provisioned or at a userspecified time.

Among the existing proactive approaches, efforts are focused onpredicting future failures with ML techniques. However, most fiberissues and fiber connection issues (e.g., fiber cuts) are notpredictable. That is, a fiber cut is the result of an external eventthat would not be predicted through ML. Even when a failure ispredicted, there is usually a lack of rational reasoning provided tonetwork operators to guide their actions to prevent the failure fromhappening. This lack of reasoning hampers the operators' ability todetect possible false positives.

The health of jump fibers (e.g., patch cords) within a node/shelf hasbeen ignored in conventional systems. Since there are many more intra-NEconnections than line fiber connections in an optical system,conventional techniques of monitoring only fiber spans can easily missmany types of potential problems that can occur in the optical system.Vulnerable intra-NE connections can also impact signal quality. Intra-NEconnections are also more accessible and require less operational effortto remedy versus inter-NE connections. Therefore, there is a need in thefield of optical networks to utilize both short-term and long-termmetrics (e.g., including a fiber span between two adjacent nodes, afiber connection between two devices within a node, fiber connectors,etc.) to allow network operators a chance to receive a comprehensiveview of the health of all fibers and fiber connections in the network.

BRIEF SUMMARY OF THE DISCLOSURE

The present disclosure relates to systems and methods for the analysisof optical fiber spans and connections using both long-term historicaldata and immediate (current) data. The systems and methods includevarious techniques to monitor for proactive analysis to ensure theperformance and reliability of all fiber connections in the opticalnetwork. This system can help network operators better understand thecondition of fiber spans and connections in order to diagnose any issuesin the network. The results of the proposed system can help the networkoperators to understand the health of the fibers and fiber connectionsof their entire network, prioritize fiber maintenance, as well as makerouting/restoration decisions.

The present disclosure may be directed to systems, methods, andcomputer-readable media for logging long-term data, performing fiberconnection analysis, and displaying analysis reports on an interactiveUser Interface (UI) device. A system, according to one implementation,may include a network interface arranged in communication with anoptical network for obtaining data associated with performance of fibersand fiber connections. For example, the optical network may include atleast an inter-node fiber connecting two adjacent network nodes andintra-node fibers connecting between photonic components within eachnetwork nodes. The system may also include an interactive userinterface, a processing device, and a memory device. The memory devicemay be configured to store computer logic having instructions that, whenexecuted, enable the processing device to log the data obtained by thenetwork interface over time in the memory device as historical data. Theinstructions also enable the processing device to analyze the health ofthe fibers and fiber connections based on the historical data andimmediate data newly obtained by the network interface. Finally, theinstructions enable the processing device to display a report on theinteractive user interface, where the report may be configured to showthe health of all optical fibers and fiber connections in the network.

According to some embodiments, the optical network may include one ormore fiber connections to be evaluated. The network interface may beconfigured to obtain the data on a periodic basis. The data may includePerformance Metric (PM) data, parameters, alarms, and metadataassociated with the performance of all fibers and fiber connections. Insome embodiments, the instructions may further enable the processingdevice to determine baseline values, averages, minimums, maximums, andtrends from the historical data. The processing device may further beconfigured to perform a risk assessment based on the health of eachfiber connections and the importance of the overlaid services.

Furthermore, this system may be configured such that analyzing thehealth of the fibers and fiber connections may include enabling theprocessing device to detect one or more issues of the fibers and fiberconnections and classifying the one or more issues. With the issuesdetected and classified, the step of displaying the report on theinteractive user interface may include providing information about thehealth of all fibers and fiber connections to allow a user to determinea root cause of the one or more issues. The one or more issues mayinclude one or more of threshold crossing events, slow trends over time,and recent sudden change events. The processing device may be configuredto utilize a supervised Machine Learning (ML) technique to classify theone or more issues and may further utilize one or more of expert rulesand labels provided by a network operator. The one or more issues of thefibers and fiber connections may include multiple issues, which may beprioritized. Also, the interactive user interface may be configured todisplay the multiple issues in the report to show the prioritization.Considering the detected issues and classification, the instructions mayfurther enable the processing device to detect a severity or importanceof the one or more issues based on one or more of customer priorities,Service Level Agreements (SLAs), and feedback from previous results.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated and described herein withreference to the various drawings, in which like reference numbers areused to denote like system components/method steps, as appropriate, andin which:

FIG. 1 is a block diagram illustrating an embodiment of a computersystem that may be utilized at a Network Operations Center (NOC) foranalyzing health of all fibers and fiber connections within an opticalnetwork, according to various embodiments.

FIG. 2 is a diagram illustrating an example of fibers and fiberconnections in an optical network including both inter-NE fiber andfiber connection and intra-NE fiber and fiber connection, according tovarious embodiments.

FIG. 3A is a graph showing data obtained from an example of dailyaverage span loss performance over time, which is one of the metricsanalyzed for health of fibers and fiber connections, according tovarious embodiments.

FIG. 3B shows a graph of a first use-case with Optical Time-DomainReflectometry (OTDR) measurements performed in a lab environment,according to various embodiments.

FIG. 3C shows the Delay Measurement (DM) average from an OpticalSupervisory Channel (OSC) in the network, according to variousembodiments.

FIG. 3D shows the DM maximum—minimum from OSC in the network, accordingto various embodiments.

FIG. 3E shows the ODU DM average in the network, according to variousembodiments.

FIG. 3F shows the ODU DM maximum—minimum in the network, according tovarious embodiments.

FIG. 4A is a graph showing another use-case where there is a suddenfluctuation followed by an increase in span loss, which may be theresult of a fiber disconnection or fiber cut event, which was notre-connected or fixed properly, according to various embodiments.

FIG. 4B is a diagram of an optical network that may be configured foranalysis in the use-case of FIG. 4A, according to various embodiments.

FIG. 5 is a graph showing a slight increase in a span loss withoutsudden activity, according to various embodiments.

FIG. 6 is the zoom-in view of FIG. 5 showing the span loss performance,according to various embodiments.

FIG. 7 is a graph showing a detectable trend in the span loss, accordingto various embodiments.

FIG. 8A is a graph showing long-term instability according to anotheruse-case, according to various embodiments.

FIG. 8B is a table showing the results of performing malicious movementon different fibers in a laboratory setting, according to variousembodiments.

FIG. 8C shows the DGD average—OCH in the network, according to variousembodiments.

FIG. 8D shows the DGD maximum—minimum in the network, according tovarious embodiments.

FIG. 8E shows the high correction count seconds from the network,according to various embodiments.

FIG. 9A is a graph showing an example of data obtained from a network todemonstrate another use-case, according to various embodiments.

FIG. 9B is a graph 70 showing results of an OTDR scan, according tovarious embodiments.

FIG. 10 is a graph showing an example of Insertion Loss (IL) performanceof an intra-NE fiber connection between an EDFA card and a Ramanamplifier card, according to various embodiments.

FIG. 11 is a diagram showing a system including steps for monitoring andanalyzing the fiber and fiber connections of an optical network,according to various embodiments.

FIG. 12 is a flow diagram showing a process for performing an analysisof fibers and fiber connections (e.g., including both inter-node andintra-node fibers and fiber connections) for each individual fiber path,according to various embodiments.

FIG. 13 is a graph showing the change in loss with respect to an initialbaseline value, according to various embodiments.

FIGS. 14A and 14B are graphs showing fluctuating signal powertransmitted over the fiber, according to various embodiments.

FIG. 15 is a graph showing an example of an IL slow trend, according tovarious embodiments.

FIG. 16 is a graph showing an example of sudden fluctuations in the ILparameter, according to various embodiments.

FIG. 17 is a diagram illustrating a screenshot of a User Interface (UI),according to various embodiments.

FIG. 18 is a flow diagram showing a process for handling short-term andlong-term data associated with an optical system having a number ofoptical fibers and components and displaying results of fiber connectionanalysis procedures on a UI, according to various embodiments.

FIG. 19 is a flow diagram showing a generalized process, according tovarious embodiments.

DETAILED DESCRIPTION OF THE DISCLOSURE

In various embodiments, the present disclosure relates to systems andmethods for logging long-term historical data related to all fibers andfiber connections of an optical network. For example, one opticalnetwork may include a fiber (e.g., a fiber span, a buried optical fiberline, an aerial optical fiber line, a submarine optical fiber line,etc.) that connects two adjacent network nodes. According to the presentembodiments, the fiber of the optical network not only include thisinter-node fibers, but it also includes the intra-node fibers. Forexample, the intra-node fiber may include fiber patch cords, jumpfibers, connectors, optical interfaces, fiber distribution elements,etc.

Issues with fibers and fiber connections are one of the leading causesof service disruption in optical networks. Thus, there is a need toprovide more visibility on the quality of the fibers and fiberconnections of an optical network to a network operator associated withthe optical network. When informed with problematic fibers and/orpotential future issues as described in the present disclosure, networkoperators can easily diagnose certain issues with the Network Elements(NEs) and fiber links in order to take remedial actions before majorfailures happen.

However, as mentioned above, most optical networks only raise alarms atthe moment when a hard-coded threshold is crossed. Thus, mostconventional systems only consider a present-tense view of fibermonitoring. Hence, they are blind to long-term degradation effects. Theembodiments of the present disclosure rectify these issues by providingthe valuable analysis of long-term historical trends.

Some conventional fiber monitoring systems focus on long fiber linksbetween NEs (i.e., inter-NE links). Although there is value in detectingissues with inter-NE links, the embodiments of the present disclosureare further configured to detect issues with “intra-NE” fibers and fiberconnections within each of the two adjacent NEs (or nodes) connected viathe inter-NE links. The intra-NE fibers and fiber connections mayinclude connectors, ports, fiber patch cords, jump fibers, fiberinterfaces, fiber distribution components, and other optical fiberconnection components. It should be noted that monitoring intra-NEfibers and fiber connections, which are arranged between devices withina NE, may be equally as important as the monitoring of other components(e.g., inter-NE fiber), which will also help ensure the quality andreliability of optical signal transmission.

Finally, predictive approaches have started to attract more interests inthe field of optical networks. However, since it may be difficult forconventional systems to accurately predict certain future fiberconnection failures (e.g., unexpected fiber cuts), it may be moreeffective (and more beneficial to network operators) to provide a reportbased on a comprehensive historical data analysis to help the networkoperators to understand the risks of the vulnerable connections and tomake operational decisions on their own terms.

Thus, the present disclosure provides a comprehensive fiber connectionmonitoring and analysis system for the entire optical network, includingboth the inter-NE and intra-NE fiber connections. According to theimplementations of data analysis procedures described in the presentdisclosure, the performance of fibers and fiber connections can beevaluated. From the evaluations detected over time, vulnerable fibersand fiber connections can be identified, classified, and rated. Theembodiments of the present disclosure are configured to report theresults of the analysis of the health of fibers and fiber connectionsfor both immediate issues (e.g., fiber pinch condition, etc.) andlong-term issues (e.g., accumulation of bad splices over time, etc.).Users (e.g., network operators at Network Operations Centers (NOCs),data centers, etc.) will be able to review the summary of health offibers and fiber connections of the entire network to understand thequality of each of the fiber components in their network. For example,the reports may be displayed on an interactive user interface (e.g.,graphical user interface) in order to enable the user to sort or groupdifferent criteria as he or she wishes. This may allow the user to rankand evaluate the risk of the vulnerable connections on their own termsand prioritize potential issues and remedial actions.

The present disclosure may be configured to utilize Machine Learning(ML) to detect and analyze various metrics of the optical network overtime. In some embodiments, the systems and methods may include asupervised ML technique where a ML model can be trained using expertrules, labels, classifications, and other input from an expert (e.g.,network operator). The operator feedback collected through ticketingsystems may be used to label the different classes of fiber issues. Inprinciple, with enough feedback/labels, a supervised ML approach couldbe utilized to classify the different classes of problematic fibers.With the addition of cross-layer topology information, it is possible tocompare fiber loss degradation with optical margins at receiver OpticalChannel Laser Detector (OCLD) cards (and their overlaid services). Thisenables precise risk assessment and state-of-the-art service assurance,taking into account the type of fiber issues and the optical marginsavailable.

The systems and methods for monitoring the health of fibers and fiberconnections thereby provide a tool for alerting the network operator ofvarious conditions and allowing the network operator to be proactivewith respect to potential fiber issues. Furthermore, the results of thesystems and methods can help the network operators understand the healthof the fibers, fiber connections, etc. of their entire network,prioritize the maintenance of the fibers and fiber connections asneeded, and make routing/restoration decisions.

Network Operations Center (NOC)

FIG. 1 is a block diagram illustrating an embodiment of a computersystem 10 that may be utilized at a Network Operations Center (NOC) foranalyzing fibers and fiber components of an optical network within anoptical network. In the illustrated embodiment, the computer system 10may be a digital computer that, in terms of hardware architecture,generally includes a processing device 12, a memory device 14,Input/Output (I/O) interfaces 16, a network interface 18, and a database20. The memory device 14 may include a data store, database (e.g.,database 20), or the like. It should be appreciated by those of ordinaryskill in the art that FIG. 1 depicts the computer system 10 in asimplified manner, where practical embodiments may include additionalcomponents and suitably configured processing logic to support known orconventional operating features that are not described in detail herein.The components (i.e., 12, 14, 16, 18, 20) are communicatively coupledvia a local interface 22. The local interface 22 may be, for example,but not limited to, one or more buses or other wired or wirelessconnections. The local interface 22 may have additional elements, whichare omitted for simplicity, such as controllers, buffers, caches,drivers, repeaters, receivers, among other elements, to enablecommunications. Further, the local interface 22 may include address,control, and/or data connections to enable appropriate communicationsamong the components 12, 14, 16, 18, 20.

The processing device 12 is a hardware device adapted for at leastexecuting software instructions. The processing device 12 may be anycustom made or commercially available processor, a Central ProcessingUnit (CPU), an auxiliary processor among several processors associatedwith the computer system 10, a semiconductor-based microprocessor (inthe form of a microchip or chip set), or generally any device forexecuting software instructions. When the computer system 10 is inoperation, the processing device 12 may be configured to executesoftware stored within the memory device 14, to communicate data to andfrom the memory device 14, and to generally control operations of thecomputer system 10 pursuant to the software instructions.

It will be appreciated that some embodiments of the processing device 12described herein may include one or more generic or specializedprocessors (e.g., microprocessors, CPUs, Digital Signal Processors(DSPs), Network Processors (NPs), Network Processing Units (NPUs),Graphics Processing Units (GPUs), Field Programmable Gate Arrays(FPGAs), and the like). The processing device 12 may also include uniquestored program instructions (including both software and firmware) forcontrol thereof to implement, in conjunction with certain non-processorcircuits, some, most, or all of the functions of the methods and/orsystems described herein. Alternatively, some or all functions may beimplemented by a state machine that has no stored program instructions,or in one or more Application Specific Integrated Circuits (ASICs), inwhich each function or some combinations of certain of the functions areimplemented as custom logic or circuitry. Of course, a combination ofthe aforementioned approaches may be used. For some of the embodimentsdescribed herein, a corresponding device in hardware and optionally withsoftware, firmware, and a combination thereof can be referred to as“circuitry” or “logic” that is “configured to” or “adapted to” perform aset of operations, steps, methods, processes, algorithms, functions,techniques, etc., on digital and/or analog signals as described hereinfor the various embodiments.

The I/O interfaces 16 may be used to receive user input from and/or forproviding system output to one or more devices or components. User inputmay be provided via, for example, a keyboard, touchpad, a mouse, and/orother input receiving devices. The system output may be provided via adisplay device, monitor, Graphical User Interface (GUI), a printer,and/or other user output devices. I/O interfaces 16 may include, forexample, one or more of a serial port, a parallel port, a Small ComputerSystem Interface (SCSI), an Internet SCSI (iSCSI), an AdvancedTechnology Attachment (ATA), a Serial ATA (SATA), a fiber channel,InfiniBand, a Peripheral Component Interconnect (PCI), a PCI eXtendedinterface (PCI-X), a PCI Express interface (PCIe), an InfraRed (IR)interface, a Radio Frequency (RF) interface, and a Universal Serial Bus(USB) interface.

The network interface 18 may be used to enable the computer system 10 tocommunicate over a network, such as an optical network, the Internet, aWide Area Network (WAN), a Local Area Network (LAN), and the like. Thenetwork interface 18 may include, for example, an Ethernet card oradapter (e.g., 10BaseT, Fast Ethernet, Gigabit Ethernet, 10GbE) or aWireless LAN (WLAN) card or adapter (e.g., 802.11a/b/g/n/ac). Thenetwork interface 18 may include address, control, and/or dataconnections to enable appropriate communications on the network.

The memory device 14 may include volatile memory elements (e.g., RandomAccess Memory (RAM)), such as Dynamic RAM (DRAM), Synchronous DRAM(SDRAM), Static RAM (SRAM), and the like, nonvolatile memory elements(e.g., Read Only Memory (ROM), hard drive, tape, Compact Disc ROM(CD-ROM), and the like), and combinations thereof. Moreover, the memorydevice 14 may incorporate electronic, magnetic, optical, and/or othertypes of storage media. The memory device 14 may have a distributedarchitecture, where various components are situated remotely from oneanother, but can be accessed by the processing device 12. The softwarein memory device 14 may include one or more software programs, each ofwhich may include an ordered listing of executable instructions forimplementing logical functions. The software in the memory device 14 mayalso include a suitable Operating System (O/S) and one or more computerprograms. The O/S essentially controls the execution of other computerprograms, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices. The computer programs may be configured to implement thevarious processes, algorithms, methods, techniques, etc. describedherein.

The memory device 14 may include a data store used to store data. In oneexample, the data store may be located internal to the computer system10 and may include, for example, an internal hard drive connected to thelocal interface 22 in the computer system 10. Additionally, in anotherembodiment, the data store may be located external to the computersystem 10 and may include, for example, an external hard drive connectedto the Input/Output (I/O) interfaces 16 (e.g., SCSI or USB connection).In a further embodiment, the data store may be connected to the computersystem 10 through a network and may include, for example, a networkattached file server.

Moreover, some embodiments may include a non-transitorycomputer-readable storage medium having computer readable code stored inthe memory device 14 for programming the computer system 10 or otherprocessor-equipped computer, server, appliance, device, circuit, etc.,to perform functions as described herein. Examples of suchnon-transitory computer-readable storage mediums include, but are notlimited to, a hard disk, an optical storage device, a magnetic storagedevice, a Read Only Memory (ROM), a Programmable ROM (PROM), an ErasablePROM (EPROM), and Electrically Erasable PROM (EEPROM), Flash memory, andthe like. When stored in the non-transitory computer-readable medium,software can include instructions executable by the processing device 12that, in response to such execution, cause the processing device 12 toperform a set of operations, steps, methods, processes, algorithms,functions, techniques, etc. as described herein for the variousembodiments.

The computer system 10 may further include a comprehensive fibers andfiber connections health analyzer 24, which may be implemented inhardware, software, firmware, or any combination thereof. Asillustrated, the comprehensive fiber and fiber connection healthanalyzer 24 may implemented as software and/or firmware and stored inthe memory device 14 or other non-transitory computer-readable media.The comprehensive fiber and fiber connection health analyzer 24 mayinclude computer logic having instructions that, when executed, enablethe processing device 12 to perform certain logic functions.

FIG. 2 is a diagram illustrating an embodiment of a portion of a network30 having multiple fibers and fiber connections in a portion of anoptical network 32 extending from a first node 34 (NODE A) to a secondnode 36 (NODE B) operating in the network 30. The fibers and fiberconnections in the portion of an optical network 32 may includeintra-node fiber elements 38 of the first node 34, an inter-node fiberspan 40 connected the first and second nodes 34, 36 together, andintra-node fiber elements 42 of the second node 36. In addition, thefibers and fiber connections in the portion of an optical network 32further include a first fiber connection 44 (or connector) that connectsthe first node 34 with the inter-node fiber span 40 and a second fiberconnection 46 (or connector) that connects the inter-node fiber span 40with the second node 42.

The network interface 18 shown in FIG. 1 is configured to communicatewith the first and second nodes 34, 36 to obtain Performance Monitoring(PM) parameters and alarms related to performance the fibers and fiberconnections 30. Then, the comprehensive fiber and fiber connectionhealth analyzer 24 shown in FIG. 1 is configured to allow the processingdevice 12 to log the PM parameters and alarms in the memory device 14and/or database 20. Also, the processing device 12 may be configured toanalyze the PM parameters and alarms to derive additional variables thatmay be used to detect or classify various fiber issues. For example,root causes of various fiber issues of the fibers and fiber connections30 can be defined ahead of time. As such, the processing device 12 cancompare the PM parameters and other derived variables of currentlymeasured PM parameters with historical data to determine short-term andlong-term issues. This information can then be presented on an I/Ointerface 16, such as a User Interface (UI), Graphical User Interface(GUI), or the like.

Root Causes

Different fiber problems may be exposed as a result of various rootcauses. Root causes can be defined as instantaneous (or short-term)issues and long-term issues. An example of some instantaneous issuesincludes a) fiber cuts, b) dirty fibers, c) dirty connectors, d) looselyconnected fiber, e) pinched, bent, or kinked fibers, f) fibers beingphysically moved, g) fiber being intruded or tapped, and others. Anexample of some long-term issues includes: a) bad fiber repairs (e.g.,“splicing”), b) manufacturing defects, c) abnormal fiber aging, d)addition of new malicious fibers, e) addition of multiple issues overtime (e.g., multiple splices), and others. These different root causestend to produce different data patterns, as can be seen with respect toFIGS. 3-9. The comprehensive fiber and fiber connection health analyzer24 may be driven by a combination of short-term and long-term data todetect and classify fiber issues.

As described with respect to FIGS. 3-9, the embodiments of the presentdisclosure may be applicable to four different categories of use-cases.The four use-cases may include:

-   1) detecting a sudden Loss of Signal (LOS), such as a fiber cut,    un-expected disconnection of fiber, etc.;-   2) determining if fiber is being physically moved or stressed;-   3) detecting long-term fiber degradation; and-   4) detecting fiber intrusion, such as, adding additional fiber,    performing a fiber-tapping procedure (e.g., “micro bending,” etc.).

Detecting LOS may be a relatively easy diagnosis. Determining when afiber is physically moved or stressed may be include a medium difficultydiagnosis. Detecting long-term fiber degradation may use software forpredicting the health of the network, which may include a relativelyeasy diagnosis. The fiber-tapping procedure may be a state-of-the-artprocedure and may be relatively more difficult to execute.

FIG. 3A is a graph showing data obtained from an example of over timespan loss performance of an inter-NE fiber. The graph of FIG. 3A showsan example of a first use-case for analyzing a sudden Loss of Signal(LOS). In FIG. 3A, OPOUTAVG-OTS_6 is the daily average input power of aninter-NE fiber span reported by the transmit amplifier at upstream,OPINAVG-OTS_8 is the daily average output power of the same inter-NEfiber span reported by the receiver amplifier at downstream. Span lossis calculated by the difference between the input power (OPOUTAVG-OTS_6)and output power (OPINAVG-OTS_8) of the fiber span. For a fiber cutevent, in addition to being recorded by PM's, a receiver amplifier ofthe problematic fiber span may raise an “optical line failure” flag andautomatically shut off. Both the PM's and the alarms can be used as thesignature of a fiber cut event.

FIG. 3B shows a graph 50 of the first use-case with Optical Time-DomainReflectometry (OTDR) measurements performed in a lab environment. Withthe additional 550 meters of additional fiber, a clean signature withmay be obtained before and after. A first event 52 shows the visibleeffect of adding 500 meters, which may be detected by standard OTDR. Asecond event 54 shows a reflection evented detected by standard OTDR,where the distance is moved from 20.536 km to 21.046 km. The secondevent 54 includes a clean signature that the additional fiber was addedin the path.

In order to detect additional fiber in a fiber span when OTDRmeasurement (graph 50) is not available, combination of OpticalSupervisory Channel (OSC) delay measurements, Optical-channel Data Unit(ODU) delay measurements, and Optical Power Received (OPR) variation canalso be used. However, there may be measurement error or ambiguity withrespect to interpreting the measurement data from different sources. Insome cases, it may be better to combine all available inputs in a MLclassifier trained specifically for this use-case.

FIGS. 3C-3F show the first use-case with a standard software program forpredicting the health of a network for an example customer. The graphsshow frequency versus time. OSC and ODU delay measurements may becollected and analyzed immediately. However, variations of DM can becaused by various effects. The computer system 10 may train and use acustomized ML model to determine classifications among them. FIG. 3Cshows the DM average from OSC in the network. FIG. 3D shows the DMmaximum—minimum from OSC in the network. FIG. 3E shows the ODU DMaverage in the network. Also, FIG. 3F shows the ODU DM maximum—minimumin the network.

FIG. 4A is a graph showing another use-case where there is an increasein span loss after a sudden fluctuation, which may be the result of afiber disconnection or fiber cut event, which was not re-connected orfixed properly. In this case, a baseline may be calculated and span lossmay be monitored overtime and compared with the baseline to determine aseverity based on the difference. The difference may be calculated as adelta of 0.5 dB, 1 dB, 2 dB, etc.

The use-case of FIG. 4A may also apply to a a defect from splicing, aloosely connected fiber, or other condition where the attenuation may bevery high. An Optical Return Loss (ORL), in which a higher valueindicates better performance. Also, this use-case may be related to adirty fiber or dirty connector. In the case of a dirty fiber orconnector, an ORL may be very low (e.g., below 17 dB). Furthermore, thisuse-case may apply to a situation where the fiber is pinched, bent, orkinked, which can be presented in the graph.

The PM parameters measured from the network may include DGDMAX, DGDAVG,SPANLOSS, ORL, Tx/Rx for Spans (topology), PRFBER, QAVG, QSTD. From thisdata, OSC span loss may be calculated, which, according to someembodiments, may not simply be the calculation of a difference.

FIG. 4B is an optical network 60 that may be configured for analysis inthe use-case of FIG. 4A. The graph of FIG. 4A may also apply torepresentation on a display that may be viewed for diagnosing an agedfiber, a moved fiber (e.g., environmental factors), or othercharacteristics of a fiber in the optical network 60. In this example,the optical network 60 may include a number of Wavelength SelectiveSwitching (WSS) devices 62, amplifiers 64, a ChannelMultiplexer/Demultiplexer (CMD) device 66 and a number of OpticalChannel Laser Detectors (OCLDs) 68 in a Layer 0. The OCLD 68 may beconnected with a plurality of user or client devices in a Layer 1 andabove. Data may be obtained for monitoring fiber and fiber connectionperformance, including power/loss related parameters, polarizationrelated parameters, delay related parameters and periodic OTDR traces.

FIGS. 5-7 show graphs according to another use-case. FIG. 5 is a graphshowing a slight increase in a span loss. For example, this may be theresult of a slight moved or stressed fiber. FIG. 6 is the zoomed in ofthe span loss trace of FIG. 5. FIG. 7 is a graph showing a detectabletrend in the span loss after being discontinued for a day at day 105.This may be the result of the fiber not being connected or splicedproperly after day 105, for example.

FIG. 8A is a graph showing long-term instability according to anotheruse-case. For example, the monitored results may show the results of aroot-cause where an aerial fiber is physically moved. The graph of FIG.8 can be viewed to detect that physical movement of a fiber hasoccurred. A challenge in this respect, however, is that it may bedifficult to differentiate between normal movement and maliciousmovement. In some embodiments, the computer system 10 may be configuredto train and use a dedicated ML model for differentiating between normaland malicious movement.

FIG. 8B is a table showing the results of performing malicious movementon (e.g., by physically kicking) different fibers in a laboratorysetting. In this example, it is shown that physical movement of thefiber can be detectable.

FIGS. 8C-8E are graphs showing measurement of frequency versus time foran example network. FIG. 8C shows the DGD average—OCH in the network.FIG. 8D shows the DGD maximum—minimum in the network. Also, FIG. 8Eshows the high correction count seconds from the network.

FIG. 9A is a graph showing an example of data obtained from a network todemonstrate another use-case. In this example, a trending span loss maybe detected by viewing the results. For example, the trending span lossmay be a sign of a slow linear degradation of a fiber (e.g., fiberaging) or a long-term fiber degradation pattern.

Various PM parameters or metrics may be detected at various points inthe network for analysis of the fibers and fiber connections 30 of anoptical network. For example, fiber loss metrics may be obtained bymeasuring total optical power at various ports in the network, measuringOSC span loss, and/or measuring other span losses. Various fibers mayhave different fiber types, such as NDSF, TWC, TWP, ELEAF, LS, TWRS,LEAF, TERALIGHT, etc.

Another additional use-case may include fiber tapping. For example,fiber tapping detection can be done with a combination of precise OTDRand advanced analytics, as suggested in M. Zafar Iqbal et al., OpticalFiber Tapping: Methods and Precautions. However, this use-case mayrequire dedicated lab study and partnership with the client to definewhat specific tapping methods need to be tested.

The continuous and period monitoring of PM metrics, alarms, metadata,etc. of the optical network 32 includes detecting the characteristicsand conditions of the fibers and fiber connections 30, which include theinter-node fibers and fiber connections and intra-node fibers and fiberconnections. In some situations, OTDR traces may be run on a periodicbasis to detect and localize change over inter-NE fibers and to monitorfiber loss degradation.

FIG. 9B is a graph 70 showing results of an OTDR scan. The graph 70shows attenuation (dB) versus distance along the optical fiber span. TheOTDR scan can be displayed (e.g., on a UI of the I/O interfaces 16) forallowing the user to determine various events or characteristics of thenetwork. For example, the graph 70 of the OTDR scan may expose a loss offiber connection 72, a back-reflection of a fiber connection 74, a slope76 for allowing a unit fiber loss (e.g., aging) to be detected, amongother characteristics. The changes of the OTDR trace over time mayindicate various events that may be happening to the inter-node fibers40.

FIG. 10 is a graph showing an example of Insertion Loss (IL) performanceof an intra-NE fiber connection over 147 days of connection between port1-6-5 of an EDFA card and port 1-5-6 of a Raman amplifier card. In thiscase, the IL fluctuates significantly. Because the hard-coded thresholdfor high IL between intra-NE connection is 1.5 dB in the system, thereis no alarm or warning for this connection. However, this IL fluctuationis enough for causing fluctuation of signal performance. This exampledemonstrates being able to proactively monitor the health of theintra-NE jump fibers and identify the vulnerable ones.

According to other embodiments, Polarization effects can be monitored todetermine the health of fibers and fiber connections 30 of an opticalnetwork 32. For example, if State of Polarization (SOP) parameters arepresented at every span, SOP transients can be detected.

Polarization Mode Dispersion (PMD) is another Polarization Effectsparameter that can be detected from PM data at the Receiver of anoptical signal. PMD occurs in single-mode fibers. It is the delaybetween two polarization modes, captured as Differential Group Delay(DGD). DGD and CD provides a good indication that something has changed.Certain factors may contribute to PMD, such as a) bit rate of thesignal, b) fiber core symmetry, c) environmental factors, d) bends orstress in the fiber, and others. If high PMD is measured at theReceiver, compensation for PMD may be required when the bit rate isgreater than 40 Gbps. If abnormal PMD change and trend is detected atthe receiver of an optical channel, it indicates changes of one or moreof the fibers that transmitting the optical channel.

The SOP and PMD parameters can be provided to the user (e.g., networkoperator) by displaying the results on a user interface (e.g., I/Ointerface 16). In response to analyzing these results, the operator maydetermine or recommend certain solutions for minimizing the SOP and PMD.For example, some solutions may include employing next generationoptical transmitters and receivers, employing improved fibers, employinga manufacturer's recommended installation techniques for fibers, orother various actions.

Therefore, according to various embodiments, the computer system 10 ofthe NOC may not specifically monitor PMs but will be able to receive PMsvia the network interface 18. For example, this may be accomplishedusing a telemetry process. The NOC can then react to alarms, as needed,to solve certain issues in the network. The user at the NOC can view thedata, which may be presented in graphs, tables, etc., as describedabove. From the presented data, the user can analyze the trends andchanges in PMs.

Multiple conditions may be observed based on the root-cause of fibersand/or connectors being dirty, bent, disconnected, pinched, etc. Theseconditions may reference various alarms, such as a) Automatic PowerReduction (APR) Active (e.g., regarding an amplifier (EDFA, Raman),Variable Optical Amplifier (VOA), amplifier monitor, etc.), b) SignalDegrade on ETH, ETH100G, ETH10G, WAN, etc., c) Signal Degrade on OC/STM,STTP, STS/HO VC, VT/LO VC, etc., d) Signal Failure on OC/STM, STTP,etc., e) Excessive Error Rate on STS/HO VC, VT/LO VC, etc., f) GaugeThreshold Crossing Alert Summary (e.g., related to AMP, VOA, RAMAN,OTDRCFG, OPTMON, etc.), g) Group Loss of Signal, h) High Fiber Loss, i)High Received Span Loss, j) Input Loss of Signal of amplifier, k) Lossof Frame and Multi-frame (OTUTTP, ETTP), l) Loss of Frame, m) Loss ofMulti-frame, n) Output Loss of Signal, o) Raman Failed to Turn On, p)Shut-off Threshold Crossed, q) Input Loss of Signal, r) Loss of Signal,s) Loss of Synchronization Messaging Channel, t) Low Optical Return Lossat Input, u) Low Optical Return Loss at Output, v) ODU Signal Degrade,w) ODU Signal Fail, x) OSC Loss of Signal, y) OSC Signal Degrade, z) OTUSignal Degrade, aa) OTU Signal Fail, ab) Loss of Lock (e.g., bent,coiled, etc.), ac) Loss of OPU Multi-frame Identifier, and others.

According to the various embodiments of the present disclosure, thecomprehensive fiber and fiber connection health analyzer 24 isconfigured to support a fiber health analyzing system to log PM dataobtained over time regarding the optical network including bothinter-node and intra-node fibers and fiber connections. Data is obtainednot only from line fibers, but also from jump fiber connections within ashelf or node itself. Logging and displaying the short-term andlong-term data of the photonic components can provide a valuable toolfor network operators to see short-term events and long-term trendswhich may indicate that the fiber and fiber connections may bevulnerable to current or future issues or may be presently problematic.As a result of the computer system 10 providing this useful informationin comprehensive displays, the network operator can response in anynumber of ways to remedy detectable current or future issues.

Data can be obtained from various monitoring sites throughout thenetwork. For example, PM data may be obtained from input ports andoutput ports of the nodes 34, 36 and/or other ports associated withIntermediate Line Amplifiers (ILAs), intra-node components, etc. Themonitoring devices may obtain Optical Power In, Optical Power Out,Optical Return Loss (ORL), OTDR trace information, OSC delaymeasurement, OSC span loss measurement, SOP measurements, etc. Thecomprehensive fiber and fiber connection health analyzer 24 may enablethe processing device 12 to then use any suitable methods to constructadditional information or metadata from the combination of multiple datasources.

Additionally, the monitoring device may be configured to obtainadjacency (or topology) information to determine how ports are connectedin transmit-receive pairs. The procedures of the comprehensive fiber andfiber connection health analyzer 24 may be used for analyzing thecomprehensive historical data of the quality of fibers, fiberconnections, to thereby identify vulnerable fiber and fiber connectionsthat may need attention (e.g., maintenance, replacement, etc.). Theanalysis includes not only the instant (or newly obtained) data, butalso historical averages and/or trends. The analysis may also detectORL, and/or OSC delay measurement, OSC span loss measurement, tidemarking, alarm data, etc., along with derived metrics which may be usedto help discover more fiber problems.

In addition to obtaining, logging, and presenting PM data, thecomprehensive fiber and fiber connection health analyzer 24 may beconfigured to utilize Machine Learning (ML) techniques, algorithms,models, etc., which may be trained based on historical data. Thetraining of ML models may involve a supervised training process ofreceiving expert rules, labels, tickets, etc. from one or more users.The ML techniques may include performing a classification process toaccurately classify various vulnerabilities of fibers, connectors andother fiber connections, etc. The classification process can be based onexpert rules, supervised ML trained with labels from operator feedbackin one or more ticketing systems, etc.

The comprehensive fiber and fiber health analyzer 24 may provideadditional features and results, as follows. Different visualization,sorting, and grouping flexibilities for displaying reports allows theuser to view the data based on different priorities. Risk assessment ofoverlaid services, considering, for example, optical margins at receiverOCLD, type of fiber issue, importance and number of services potentiallyaffected. Also, specific client-defined use-cases may be used to reveala malicious addition of new fibers, detect that a fiber is beingphysically moved, etc.

FIG. 11 is a diagram showing an embodiment of a system 80 (e.g.,computer system 10) including steps for monitoring and analyzing thefiber connectivity of an optical network 82. More particularly, thesystem 80 is configured for analyzing a fiber connection, which mayinclude a combination of optical fibers, connectors, etc. Monitoringdevices (not shown) throughout the optical network 82 are used to obtaindata 84 (e.g., PM data, alarms, etc.) continuously or periodically fromthe optical network 82 for processing. Block 86 includes a first stepfor “long-term data logging,” which may include logging the data 84 inthe memory device 14 and/or database 20 of the computer system 10.

Next, block 88 includes a second step for executing a “fiber connectionperformance analysis” or other analysis for determining the performanceof fiber and fiber connections. The fiber connection in this embodimentmay include fibers, fiber connectors, and/or other fiber connectioncomponents at a connection site or over the entire optical networkincluding both inter-node fibers (e.g., fiber spans, fiber links, etc.)and intra-node fibers and fiber connection components (e.g., jump fiber,patch cord, ports, connectors, Fiber Interconnection Management device(FIM), etc.). The fiber connection performance analysis may includedetermining currently-detected threshold crossings, unfavorablelong-term trends, or other features as described throughout the presentdisclosure.

The system 80 further includes block 90, which include a third step(i.e., “displaying report in interactive UI”) for displaying a report ofthe performance analysis (block 88) in an interactive User Interface(UI), such as one of the I/O interfaces 16 of the computer system 10.The comprehensive fiber and fiber connection health analyzer 24 may beconfigured to control the UI to allow it to display the various graphs,tables, etc. in any suitable format and using any suitable variables forclearly demonstrating to the user the condition of the optical network82. The UI may be interactive, allowing the user to switch betweendifferent graphs, tables, etc., zoom in, zoom out, highlight certainportions of the display, show certain characteristics, parameters,values of certain points within the display, and other suitable UIfunctions.

Thus, FIG. 11 shows the steps of fiber connection monitoring andanalysis systems. The optical network 82 reports PMs, alarms of allports and topology information. A data storage server (e.g., associatedwith the computer system 10) may be used to receive and store the datafor as long as needed. The historical data may then be pulled forperformance analysis of the fibers, fiber connectors, and other photoniccomponents along an optical path. The results may be displayed in aninteractive UI, which allows users to view the analysis results ofdifferent connections with different sorting and grouping criteria.

Long-Term Data Logging

Long-term data logging may include storing PMs, alarms, and metadata forfiber connection performance analysis in a suitable storage device(e.g., memory device 14, database 20, data storage server, etc.). Thefollowing are various types of information that may be stored on along-term basis: a) instant/average/min/max power of transmit ports(e.g., PTx, PTx_avg, PTx_min, PTx_max, etc.) at one end of the path, b)instant/average/min/max power of receive ports (e.g., PRx, PRx_avg,PRx_min, PRx_max, etc.) at the other end of the path, c)instant/average/min/max Optical Return Loss (ORL) of transmit ports(e.g., ORLTx, ORLTx_avg, ORLTx_min, ORLTx_max, etc.) at one end of thepath, d) instant/average/min/max ORL of receive ports (e.g., ORLRx,ORLRx_avg, ORLRx_min, ORLRx_max, etc.) at the other end of the path, e)alarms of faulty connections (e.g., Loss of Signal (LOS), high InsertionLoss (IL), low ORL, etc.), f) adjacency/topology information relatingtwo endpoints for each fiber or path, and other types of long-terminformation.

Analysis of the fibers and fiber connections (e.g., fiber connectionanalysis) may be performed for each individual path. The analysis mayinclude the steps of classifying and quantifying fiber issues. Aninitial classification may be based on PM behavior and may be configuredto highlight various issues (e.g., problematic fiber connections). Theinitial classification can also provide a summarization of symptoms tohelp network operators identify root-causes of the issues. This initialclassification can be tuned for accuracy over time, such that, withenough operator feedback collected through ticketing systems, supervisedML approaches can be utilized to eventually provide a more accurateclassification to define the different classes of photonic pathproblematic, which may be based on various root causes. In addition tothese generic classes, customized classes may be available for differentnetwork operators based on their own experiences with photonic pathissues.

Data processing methods may be performed (e.g., by the comprehensivefiber and fiber connection health analyzer 24) based on behavior,trends, thresholds, etc. associated with the PM data. The followingincludes examples of generic classes of fiber issues that may bedetected: a) a currently-obtained parameter of a fiber connectioncrosses a threshold, b) a slow trend over time, c) recent sudden changeswith respect historical data, etc. The data processing methods mayprovide a record of historical incidents in the photonic path, such as:a) events recorded by alarms and/or averaged PMs (e.g., PTx_avg,PRx_avg, ORLTx_avg, ORLRx_avg, etc.), b) fast events recorded by tidemarking PMs (e.g., PTx_min, PTx_max, PRx_min, PRx_max, ORLRx_min,ORLRx_max, ORLTx_min, ORLTx_max, etc.), and/or others.

In addition, the comprehensive fiber and fiber connection healthanalyzer 24 may be configured to calculate a “severity” score which maybe used for defining the urgency of any photonic path issues. Theseverity score may be computed based on the fiber issues detected andthe importance of the services that pass through the fibers, connectors,ports, or other components of a path. Weights may be applied todifferent classes of fiber issues and different services for calculatingthe severity score. These weights may be hyper-parameters of an MLprocess and can be fine-tuned, as needed, based on customer priorities,Service Level Agreements (SLAs), previous results, and/or other types ofsupervised or unsupervised feedback. A report of the photonic pathcondition may be generated at the end of each analysis which includesall the above analysis results.

Fiber Connection Performance Analysis

FIG. 12 is a flow diagram showing an embodiment of a process 100 forperforming an analysis of fiber and fiber connection performance (e.g.,including both inter-node and intra-node fibers and fiber connections)for each individual fiber path. More particularly, the process 100 isrelated to analyzing a “fiber connection,” which may include fibers,connectors, ports, and other fiber connection components. The process100 includes loading historical data of all network fibers (block 102)and computing instantaneous/average/tide-marking Insertion Loss (IL)parameters. IL parameters may be computed, for example, using thefollowing equations:

IL _(inst) =P _(Tx) −P _(Rx)  (Eq. 1)

IL _(avg) =P _(Tx_avg) −P _(Rx_avg)  (Eq. 2)

IL _(Tx_min-Rx_min) =P _(Tx_min) −P _(Rx_min)  (Eq. 3)

IL _(Tx_max-Rx_max) =P _(Tx_max) −P _(Rx_max)  (Eq. 4)

The process 100 also include calculating baseline values for IL (block104). In order to track the absolute change in loss, the analysis firstcalculates the IL/ORL at the beginning of history (ideally when thefiber path was first provisioned). This data can be used to calculateabsolute changes relative to present time as shown in FIG. 13, which isa graph showing the change in loss with respect to an initial baselinevalue.

Also, the process 100 includes block 106, which indicates a step tosearch and count for historical events. The analysis procedure,according to some embodiments, may be configured to search for thehistorical fault events, operation events, and sudden changes of thefiber and fiber connection under investigation. The criteria of theabove events, in some embodiments, may include:

A. Fiber disconnected events due to faults or operations such as:

-   -   1. If alarm of faulty connections (e.g., LOS, high IL, low ORL,        etc.) was raised on the ports.    -   2. If Rx port of the fiber reports null or very low power while        PMs of Tx port are within normal operation range.

B. Sudden change events of IL_(avg) and ORL_(avg), such as checking forsudden IL_(avg)/ORL_(avg) change over time. In the example of FIG. 13,the step of block 106 may include looking for a sudden jump instead of aslow trend. The sudden jumps can be detected via the inflection pointsof the 2nd derivative of IL_(avg)/ORL_(avg) time series.

C. Search and count for tide-marking events. For example, tide-markingevents can capture fast performance (e.g., IL/ORL) fluctuation whileaveraged performance can still be stable. Tide-marking events may becounted separately not only since they may be related to a fastconnection performance change within a data sample, but also becausethey may be related to a fast signal fluctuation. However, in thissituation, it still may be worth notifying the user of such fluctuationsin the system, which may be helpful information for debugging.Tide-marking events may be counted, for example, under the followingconditions:

-   -   1. Large delta of ORL tide-marking within a data sample, such        as:

Delta_(RxORL_tidemarking) =ORL _(Rx_Max) −ORL_(Rx_Min)>Threshold_(ORL_tidemarking)  (Eq. 5)

Delta_(TxORL_tidemarking) =ORL _(Tx_Max) −ORL_(Tx_Min)>Threshold_(ORL_tidemarking)  (Eq. 6)

where ORL_(Rx_Max), ORL_(Rx_Min), ORL_(Tx_Max), and ORL_(Tx_Min) arereported within the same time period.

-   -   2. IL tide-marking events detected. For example, if the fiber        connection is stable, even though the signal power transmitted        over the fiber is fluctuating, IL_(avg), IL_(Tx_min-Rx_min), and        IL_(Tx_max-Rx_max) shall be the same, as shown in FIG. 14A.        However, if a vulnerable connection causes quick fluctuation of        IL, tide-marking of IL will be able to capture the fluctuation        while IL_(avg), may not see the fluctuation, as shown in FIG.        14B. The IL tide-marking will most likely be indicating a        connection issue if:

IL _(Tx_min-Rx_min) >IL _(avg) >IL _(Tx_max-Rx_max)  (Eq. 7 and

Delta_(IL_tidemarking) =IL _(Tx_min-Rx_min) −IL_(Tx_max-Rx_max)>Threshold_(IL_tidemarking)  (Eq. 8)

If historical events are detected (block 108), the process 100 includereporting historical event issues (block 110). Otherwise, the process100 skips block 110.

D. The process 100 includes searching for slow trends (block 112) of ILand ORL since a last event and recording min/max deltas of slow trends.If a slow trend is detected (block 114), the process 100 includesreporting a slow trend issue (block 116). Otherwise, the process 100skips block 116.

Slow trends may be detected since the last photonic path issue (e.g.,fiber connection incident) which may be obtained by the previous step.Fiber connections with IL and ORL slowly changing may be a sign of badfiber connections or the fibers may be under varying mechanical orenvironmental impacts. An example of IL slow trend is shown in FIG. 15after a last incident. According to some embodiments, a slow trend maybe detected as follows:

Delta_(IL_slowtrend)=Max_(IL_avg(t))−Min_(IL_avg(t))>Threshold_(IL_slowtrend), t∈[t _(last_incident), now]  (Eq. 9)

Delta_(ORL_Tx_slowtrend)=MaxORL_(Tx_avg(t))−MinORL_(Tx_avg(t))>Threshold_(ORL_slowtrend), t∈[t _(last_incident), now]  (Eq. 10)

Delta_(ORL_Rx_slowtrend)=Max_(ORL_Rx_avg(t))−Min_(ORL_Rx_avg(t))>Threshold_(ORL_slowtrend), t∈[t _(last_incident), now]  (Eq. 11)

Note, because slow trend is detected since the last incident, theMax/Min delta computed by Equations 9-11 are due to slow trend.

E. The process 100 includes detecting recent sudden changes (block 118),such as recent connection performance fluctuations in last X days, whereX, in some embodiments, may be defined by a user. If recent suddenchanges are detected (block 120), the process 100 reports the recentchange issues (block 122). Otherwise, the process 100 skips block 122.

Sudden changes may be detected by comparing recent data with historicalconnection incidents. FIG. 16 is a graph showing an example of suddenfluctuations in the Insertion Loss (IL) parameter. In this case, it maybe worthwhile to give the recent fluctuations more attention. Therefore,the process 100 may detect sudden changes in the last X days separately.The recent connection fluctuations may be detected if:

Delta_(IL_sudden_fluctuation)=Max_(IL_avg(t))−Min_(IL_avg(t))>Threshold_(IL_recent_fluctuation)(where t is within the last X days)tm (Eq. 12)

Delta_(ORL_Tx_fluctuation)=Max_(ORL_Tx_avg(t))−Min_(ORL_Tx_avg(t))>Threshold_(ORL_fluctuation)(where t is within the last X days)  (Eq. 13)

Delta_(ORL_Rx_fluctuation)=Max_(ORL_Rx_avg(t))−Min_(ORL_Rx_avg(t))>Threshold_(ORL_fluctuation)(where t is within the last X days)  (Eq. 14)

F. The process 100 further includes detect if an absolute value of aninstant IL/ORL parameter crosses a threshold (block 124). If it isdetermined in block 126 that the threshold is crossed, the process 100reports an issue of the current parameters crossing the threshold (block128). Otherwise, the process 100 skips block 128.

The absolute value of the instant IL/ORL parameter may be detected ascrossing the threshold if one of the following occurs:

IL_(inst)>Threshold_(abs_IL)  (Eq. 15)

or

ORL_(inst)>Threshold_(abs_ORL)  (Eq. 16)

G. The process 100 also includes retrieving services that go through theconnection under evaluation, as indicated in block 130. Knowing theservices that go through the connection under evaluation may help acustomer evaluate the risk of the connection issues and prioritizemaintenance and/or service routing. This information may be used forcalculating a severity score (block 132) and may be included in thereport.

H. The process 100 includes compute the severity score of connection,which may be configured to consider all categories of connection issuesand statistics of historical events with weights that can be defined bynetwork operators. A general expression of the severity score, accordingto one embodiment, includes:

Severity Score=Σw _(connection_issue_i) S_(connection_issue_i) Σw_(service_j)  (Eq. 17)

where S_(connection_issue_i) is the severity score of each class ofconnection issues based on the results of steps C-F and user-definedhyper-parameters. The term w_(connection_issue_i) is the weight appliedon each class of connection issues. The term w_(servoce_j) is the weightof each service of all services j=1˜N that goes through the connection.Note, if no end-to-end topology is available, term Σ w_(service_j) canbe removed from Eq.17. All weights are configurable while having adefault value, such that the severity score can be calculated based onusers' priorities. Eqs. 18-24 below show an example of how severityscore of individual connection issues may be calculated.

A first connection issue (i.e., connection_issue_1) may be defined as anissue when a current connection parameter crosses a threshold:

$\begin{matrix}{S_{c{onnection}_{{issue}_{1}}} = {\max\left( \left\lbrack {{a1\frac{\min\left( \left\lbrack {{ILins},h_{IL_{abs}}} \right\rbrack \right)}{Th{reshold}_{Abs_{IL}}}},{a2\frac{{Threshold}_{{Abs}_{ORL}}}{\max\left\lbrack {{ORL}_{{Tx}_{inst}},h_{{ORL}_{abs}}} \right\rbrack}},{a2\frac{{Threshold}_{{Abs}_{ORL}}}{\max\left\lbrack {{ORL}_{{Rx}_{inst}},h_{{ORL}_{abs}}} \right\rbrack}}} \right\rbrack \right)}} & \left( {{Eq}.18} \right)\end{matrix}$

where h_(IL) _(abs) and h_(ORL) _(abs) are hyper-parameters that may bechosen to indicate very bad connection performance (e.g., 99% ofconnections in the network having better performance than thehyper-parameter chosen). The variables a1 and a2 are weights to balancethe contribution from the three terms, such that the three terms willhave the same maximum value, where:

$\begin{matrix}{{a1\frac{h_{IL_{abs}}}{{Threshold}_{{Abs}_{IL}}}} = {a2\frac{{Threshold}_{{Abs}_{ORL}}}{h_{{ORL}_{abs}}}}} & \left( {{Eq}.19} \right)\end{matrix}$

A second connection issue (i.e., connection_issue_2) may be defined asan issue of a slow trend over time:

$\begin{matrix}{S_{c{onnection}_{{issue}_{2}}} =} & \left( {{Eq}.20} \right)\end{matrix}$ $\max\left( \begin{bmatrix}{{b1*{\min\left( \left\lbrack {{Delta}_{{IL}_{slowtrend}},h_{{IL}_{slowtrend}}} \right\rbrack \right)}},} \\{{b2*{\min\left( \left\lbrack {{Delta}_{{{ORL}_{tx}}_{slowtrend}},h_{{ORL}_{slowtrend}}} \right\rbrack \right)}},} \\{b2*\min\left( \left\lbrack {{Delta}_{{{ORL}_{Rx}}_{slowtrend}},h_{{ORL}_{slowtrend}}} \right\rbrack \right)}\end{bmatrix} \right)$

Where h_(IL) _(slowtrend) and h_(ORL) _(slowtrend) are hyper-parametersthat may be chosen to indicate very bad connection performance (e.g.,99% of connections in the network having better performance than thehyper-parameter chosen). The variables b1 and b2 are weights to balancethe contribution from the three terms, such that the three terms willhave the same maximum value, where:

b1*h _(IL) _(slowtrend) =b2*h _(ORL) _(slowtrend)   (Eq. 21)

A third connection issue (i.e., connection_issue_3) may be defined as asudden change in the most recent X days:

$\begin{matrix}{S_{c{onnection}_{{issue}_{3}}} =} & \left( {{Eq}.22} \right)\end{matrix}$ $\max\left( \begin{bmatrix}{{c1*{\min\left( \left\lbrack {{Delta}_{{IL}_{{sudden}\_{fluctuation}}},h_{{IL}_{{sudden}\_{fluctuation}}}} \right\rbrack \right)}},} \\{{c2*{\min\left( \left\lbrack {{Delta}_{{ORL}_{{tx}_{{sudden}\_{fluctuation}}}},h_{{ORL}_{{sudden}\_{fluctuation}}}} \right\rbrack \right)}},} \\{c2*{\min\left( \left\lbrack {{Delta}_{{ORL}_{{Rx}_{{sudden}\_{fluctuation}}}},h_{{ORL}_{{sudden}\_{fluctuation}}}} \right\rbrack \right)}}\end{bmatrix} \right)$

Where h_(IL) _(sudden_fluctuation) and h_(ORL) _(sudden_fluctuation) arehyper-parameters which can be chosen to indicate very bad connectionperformance (e.g., 99% of connections in the network having betterperformance than the hyper-parameter chosen). The variables c1 and c2are weights to balance the contribution from the three terms, such thatthe three terms will have the same maximum value, where:

c1*h _(IL) _(sudden_fluctuation) =c2*h _(ORL) _(sudden_fluctuation)  (Eq. 23)

A fourth connection issue (i.e., connection_issue_4) may be defined ashaving a record of historical connection incidents:

$\begin{matrix}{S_{c{onnection}_{{issue}_{4}}} = {\min\left( \left\lbrack {{\#{of}{historical}{events}},h_{\#{of}{historical}{events}}} \right\rbrack \right)}} & \left( {{Eq}.24} \right)\end{matrix}$

Finally, the process 100 may include generating a report as indicated inblock 134 for the connection under evaluation. In addition to certaincriteria (e.g., Connection Issue type, Severity Score, number ofhistorical events and services that go through the connection, etc.) thenumber of bad connections in the same NEs may be counted, which may beassociated with the environmental impact of the NE, poor workmanship,etc. This information may help the operator to prioritize maintenance ofthe NE.

Displaying Report in Interactive UI

After the analysis of each individual connection is done, the resultswill be consolidated into an interactive UI, such as one of the I/Ointerfaces 16, a GUI, etc. The UI could be configured to display a tableview, which can be sorted based on any of the connection analysisoutputs (e.g., Severity Score, number of historical events, severity ofconnection issue types, services that go through the connection, numberof bad connections in the same NEs, etc.). The UI could also beconfigured to display a map view, which can show the network topologywith color-coded fiber connections. For example, the color-coding can bebased on any of the connection analysis outputs (e.g., Severity Score,number of historical events, severity of connection issue types,services that go through the connection, number of bad connections inthe same NEs, etc.). A time series of connection performance (e.g., IL,ORL, or other parameters, over time) may be available for display uponthe user's request. The UI may also be configured to display astatistical view of IL, ORL, etc. of all connections in the network.

FIG. 17 is a diagram illustrating a screenshot of a UI 140. In thisexample, the UI 140 shows results of predicting the health of a networkand may include a sorting of the risky fiber connections, where thefiber connections with the highest severity are displayed at the top ofthe UI 140.

FIG. 18 is a flow diagram showing another embodiment of a process 150for handling short-term and long-term data associated with a photonicpath having a number of optical components and displaying results offiber connection analysis procedures on a UI. In this embodiment, theprocess 150 includes continually and/or periodically monitoringparameters, metrics, PM data, alarms, etc. of an optical networkincluding a fiber span and one or more fiber connections, as indicatedin block 152. The process 150 may be executed for each individual fiber,between two adjacent nodes or two photonic devices within node in anoptical system.

While monitoring these parameters, the process 150 further includes thestep (block 154) of logging the monitored parameters, metrics, PM data,alarms, etc. obtained over time in long-term storage, whereby theobtained data is stored as historical data. The long-term data be PMparameters, alarms, metadata, etc., and may include ORL of transmit andreceive ports, alarms of faulty connections, identification of LOSevents, events of high IL, events of low ORL, adjacency/topologyinformation, among other data. The process 150 also includes computingbaseline values, averages, minimums, maximums, etc. from the loggeddata, as indicated in block 156. Next, the process 150 includesanalyzing the historical long-term data along with immediate (orinstant) short-term data, as indicated in block 158.

Based on the analysis of the short-term and long-term data, the process150 is able to detect and classify fiber health issues, as indicated inblock 160. Classifications of issues may include threshold-crossingevents, identified slow trends, recent sudden changes, events recordedby alarms and/or averaged PMs, fast events recorded by tide-marking PMs,etc. In some embodiments, classification may include the use of MLmodels and may further include the use of supervised data from experts,operators, managers, or others who can provide useful rules, labels,hyper-parameters, etc. In some embodiments, ML models may be used forperforming a risk assessment based on combinations of short-term data,long-term data, identified events, potential issues, trends, etc. Also,the ML models may detect severity, importance, or other factors forrating the fiber issues based on customer priorities, SLAs, feedbackfrom previous results, etc.

At this point, the process 150 is configured to display a report to showthe condition of the fiber connections including the detection andclassification of the fiber issues, as indicated in block 162. Thereport may be displayed on a suitable display screen (e.g., UI) for auser (e.g., network operator) to allow analysis by the user and/or toidentify or highlight certain conditions that may be observed as rootcauses of present or potential fiber issues.

FIG. 19 is a flow diagram showing another embodiment of a process 170.In this embodiment, the process 170 includes obtaining data associatedwith fiber connections in an optical network, as indicated in block 172.The fibers and fiber components, for example, may include at least aninter-node fiber connecting two adjacent network nodes and intra-nodefiber connection associated with one or more photonic devices of each ofthe two adjacent network nodes. The process 170 further includes loggingthe data over time as historical data, as indicated in block 174. Theprocess 170 also includes analyzing the health of the fiber and fiberconnections based on the historical data and newly-obtained data, asindicated in block 176. Finally, the process 170 includes displaying areport on an interactive user interface, whereby the report isconfigured to show the overview of health of all fiber and fiberconnections in the optical network.

According to some embodiments of the process 170, the process mayinclude one or more fiber connections to be evaluated. A networkinterface may be configured to obtain the data on a periodic basis. Thedata may include Performance Metric (PM) data, parameters, alarms, andmetadata associated with the performance of all fibers and fiberconnections in the network. In some embodiments, the process 170 mayfurther include the step of determining baseline values, averages,minimums, maximums, and trends from the historical data. The process 170may also include the step of performing a risk assessment based on thehealth of fibers and fiber connections.

Furthermore, the process 170 may be configured such that the step ofanalyzing the health of the fibers and fiber connections may includesteps of detecting one or more issues of the fibers and fiberconnections and classifying the one or more issues. With the issuesdetected and classified, the process 170 may further be defined wherebythe step of displaying the report on the interactive user interface mayinclude providing information about the health of the fibers and fiberconnections to allow a user to determine a root cause of the one or moreissues. The one or more issues may include one or more of thresholdcrossing events, slow trends over time, and recent sudden change events.The process 170 may utilize a supervised Machine Learning (ML) techniqueto classify the one or more issues and may further utilize one or moreof expert rules and labels provided by a network operator. The one ormore issues of the fibers and fiber connections may include multipleissues, which may be prioritized. Also, the interactive user interfacemay be configured to display the multiple issues in the report to showthe prioritization. Considering the detected issues and classificationof the fibers and fiber connections, the process 170 may further includedetecting a severity or importance of the one or more issues based onone or more of customer priorities, Service Level Agreements (SLAs), andfeedback from previous results.

Network operators and other users who manage NOCs, data centers, etc.continue to look for proactive approaches to ensuring the performanceand reliability of their optical network exceeds customer expectations.However, major parts of an optical network, which tend to cause issuesfor customers, are fibers and fiber connections. Therefore, instead ofmerely looking to fibers and fiber connections, the present disclosureis configured to help network operators better understand the fiberconnection qualities and issues in their networks. The presentdisclosure can also provide results (e.g., on an interactive display) tohelp the network operators understand the health of the fiberconnections, prioritize fiber component maintenance, as well as makerouting/restoration decisions.

Although the present disclosure has been illustrated and describedherein with reference to preferred embodiments and specific examplesthereof, it will be readily apparent to those of ordinary skill in theart that other embodiments and examples may perform similar functionsand/or achieve like results. All such equivalent embodiments andexamples are within the spirit and scope of the present disclosure, arecontemplated thereby, and are intended to be covered by the followingclaims.

1. A system comprising: a network interface arranged in communicationwith a network for obtaining data associated with fiber connections thatinclude a plurality of inter-node fibers and intra-node fibers; aninteractive user interface; a processing device; and a memory deviceconfigured to store computer logic having instructions that, whenexecuted, enable the processing device to log the data obtained via thenetwork interface over time in the memory device as historical data,analyze health of the fiber connections based on the historical data andimmediate data newly obtained by the network interface, and display areport on the interactive user interface, the report configured to showthe health of the fiber connections.
 2. The system of claim 1, whereinthe fiber connections include the inter-node fibers between adjacentnetwork nodes, the intra-node fibers within a network node, andassociated connectors.
 3. The system of claim 1, wherein the networkinterface is configured to obtain the data on a periodic basis.
 4. Thesystem of claim 1, wherein the data includes Performance Metric (PM)data, parameters, alarms, and topology data.
 5. The system of claim 1,wherein the instructions further enable the processing device todetermine baseline values, averages, minimums, maximums, and trends fromthe historical data.
 6. The system of claim 1, wherein the instructionsfurther enable the processing device to perform a risk assessment basedon the health of the fiber connections.
 7. The system of claim 1,wherein analyzing the health of the fiber connections includes detectingone or more issues of the fiber connections and classifying the one ormore issues as one of instantaneous issues and long-term issues.
 8. Thesystem of claim 7, wherein the instantaneous issues include any of a)fiber cuts, b) dirty fibers, c) dirty connectors, d) loosely connectedfibers, e) pinched, bent, or kinked fibers, f) fibers being physicallymoved, and g) fiber being intruded, and wherein the long-term issuesinclude any of a) bad fiber repairs including splicing, b) manufacturingdefects, c) abnormal fiber aging, d) addition of new malicious fibers,and e) addition of multiple splices over time.
 9. The system of claim 7,wherein displaying the report on the interactive user interface includesproviding comprehensive summary about the health of the fiberconnections to allow a user to determine a root cause of the one or moreissues.
 10. The system of claim 7, wherein the one or more issuesinclude one or more of threshold crossing events, slow trends over time,and recent sudden change events.
 11. The system of claim 7, wherein theprocessing device is configured to utilize a supervised Machine Learning(ML) technique to classify the one or more issues.
 12. The system ofclaim 7, wherein the instructions further enable the processing deviceto further utilize one or more of expert rules and labels provided by anetwork operator.
 13. The system of claim 7, wherein the one or moreissues of the fiber connections include multiple issues, and wherein theinstructions further enable the processing device to prioritize themultiple issues and display the multiple issues in the report to showthe prioritization.
 14. The system of claim 7, wherein the instructionsfurther enable the processing device to detect severity or importance ofthe one or more issues based on one or more of customer priorities,Service Level Agreements (SLAs), and feedback from previous results. 15.A non-transitory computer-readable medium configured to store computerlogic having instructions that, when executed, enable a processingdevice to: obtain data associated with the performance of fiberconnections of an optical network, the fiber connections include bothinter-node fibers connecting two adjacent network nodes and intra-nodefibers, log the data over time as historical data, analyze health of thefiber connections based on the historical data and newly-obtained data,and display a report on an interactive user interface, the reportconfigured to show the health of the fiber connections.
 16. Thenon-transitory computer-readable medium of claim 15, wherein the fiberconnections include the inter-node fibers between adjacent networknodes, the intra-node fibers within a network node, and associatedconnectors.
 17. The non-transitory computer-readable medium of claim 15,wherein the data includes Performance Metric (PM) data, parameters,alarms, and metadata associated with all fiber connections in thenetwork.
 18. The non-transitory computer-readable medium of claim 15,wherein the instructions further enable the processing device todetermine baseline values, averages, minimums, maximums, and trends fromthe historical data.
 19. A method comprising the steps of: obtainingdata associated with the performance of fiber connections of an opticalnetwork, the fiber connections include both inter-node fibers andintra-node fibers, logging the data over time as historical data,analyzing health of fiber connections based on the historical data andnewly-obtained data, and displaying a report on an interactive userinterface, the report configured to show the health of the fiberconnections.
 20. The method of claim 19, wherein the step of analyzingthe health of the fiber connections includes the step of detecting oneor more issues of the fiber connections and classifying the one or moreissues, and wherein the step of displaying the report on the interactiveuser interface includes providing information about the health of fiberconnections to allow a user to determine a root cause of the one or moreissues.